Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2010.11859	Not all parameters are born equal: Attention is mostly what you need	Transformers are widely used in state-of-the-art machine translation, but the key to their success is still unknown. To gain insight into this, we consider three groups of parameters: embeddings, attention, and feed forward neural network (FFN) layers. We examine the relative importance of each by performing an ablatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,456
2209.10241	Exact and sampling methods for mining higher-order motifs in large hypergraphs	Network motifs are recurrent, small-scale patterns of interactions observed frequently in a system. They shed light on the interplay between the topology and the dynamics of complex networks across various domains. In this work, we focus on the problem of counting occurrences of small sub-hypergraph patterns in very la...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	318,799
2401.05551	Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?	\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'' picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguis...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,825
2103.04412	Multimodal VAE Active Inference Controller	Active inference, a theoretical construct inspired by brain processing, is a promising alternative to control artificial agents. However, current methods do not yet scale to high-dimensional inputs in continuous control. Here we present a novel active inference torque controller for industrial arms that maintains the a...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	223,628
2211.14844	Estimating the number of communities in weighted networks	Community detection in weighted networks has been a popular topic in recent years. However, while there exist several flexible methods for estimating communities in weighted networks, these methods usually assume that the number of communities is known. It is usually unclear how to determine the exact number of communi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	333,019
2207.07221	Energy Storage State-of-Charge Market Model	This paper introduces and rationalizes a new model for bidding and clearing energy storage resources in wholesale energy markets. Charge and discharge bids in this model depend on the storage state-of-charge (SoC). In this setting, storage participants submit different bids for each SoC segment. The system operator mon...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	308,135
1909.00352	Enhancing AMR-to-Text Generation with Dual Graph Representations	Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges. To address this difficulty, we propose a novel graph-to-sequence model that encodes different but complementar...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	143,612
2302.06210	Precise Asymptotic Analysis of Deep Random Feature Models	We provide exact asymptotic expressions for the performance of regression by an $L-$layer deep random feature (RF) model, where the input is mapped through multiple random embedding and non-linear activation functions. For this purpose, we establish two key steps: First, we prove a novel universality result for RF mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,328
1010.0725	Link Prediction in Complex Networks: A Survey	Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link predic...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	7,782
2308.08451	AIGC In China: Current Developments And Future Outlook	The increasing attention given to AI Generated Content (AIGC) has brought a profound impact on various aspects of daily life, industrial manufacturing, and the academic sector. Recognizing the global trends and competitiveness in AIGC development, this study aims to analyze China's current status in the field. The inve...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	385,907
1906.01753	Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution	Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreferenc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	133,823
1910.00762	Accelerating Deep Learning by Focusing on the Biggest Losers	This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Selective-Backprop uses the output of a training example's forward pass to decide whether to use that example to compute gradients and update para...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,763
0802.4215	Equilibrium (Zipf) and Dynamic (Grasseberg-Procaccia) method based analyses of human texts. A comparison of natural (english) and artificial (esperanto) languages	A comparison of two english texts from Lewis Carroll, one (Alice in wonderland), also translated into esperanto, the other (Through a looking glass) are discussed in order to observe whether natural and artificial languages significantly differ from each other. One dimensional time series like signals are constructed u...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	1,363
2012.05062	Finite-dimensional observer-based PI regulation control of a reaction-diffusion equation	This paper investigates the output feedback setpoint regulation control of a reaction-diffusion equation by means of boundary control. The considered reaction-diffusion plant may be open-loop unstable. The proposed control strategy consists of the coupling of a finite-dimensional observer and a PI controller in order t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	210,670
2412.10180	A General Safety Framework for Autonomous Manipulation in Human Environments	Autonomous robots are projected to augment the manual workforce, especially in repetitive and hazardous tasks. For a successful deployment of such robots in human environments, it is crucial to guarantee human safety. State-of-the-art approaches to ensure human safety are either too restrictive to permit a natural huma...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	516,817
2209.11902	Learning Chess With Language Models and Transformers	Representing a board game and its positions by text-based notation enables the possibility of NLP applications. Language models, can help gain insight into a variety of interesting problems such as unsupervised learning rules of a game, detecting player behavior patterns, player attribution, and ultimately learning the...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	319,342
1310.7829	About Summarization in Large Fuzzy Databases	Moved by the need increased for modeling of the fuzzy data, the success of the systems of exact generation of summary of data, we propose in this paper, a new approach of generation of summary from fuzzy data called Fuzzy-SaintEtiQ. This approach is an extension of the SaintEtiQ model to support the fuzzy data. It pres...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	28,063
1101.0211	Spectral Properties of Directed Random Networks with Modular Structure	We study spectra of directed networks with inhibitory and excitatory couplings. We investigate in particular eigenvector localization properties of various model networks for different value of correlation among their entries. Spectra of random networks, with completely uncorrelated entries show a circular distribution...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,689
2301.09702	Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification	Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain. One major issue with many unsupervised re-identification methods is that they do not perform well relative to large domain var...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	341,572
1803.10678	A Mixed-Logical-Dynamical model for Automated Driving on highways	We propose a hybrid decision-making framework for safe and efficient autonomous driving of selfish vehicles on highways. Specifically, we model the dynamics of each vehicle as a Mixed-Logical-Dynamical system and propose simple driving rules to prevent potential sources of conflict among neighboring vehicles. We formal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	93,740
1407.6748	Enhancing the Accuracy of Biometric Feature Extraction Fusion Using Gabor Filter and Mahalanobis Distance Algorithm	Biometric recognition systems have advanced significantly in the last decade and their use in specific applications will increase in the near future. The ability to conduct meaningful comparisons and assessments will be crucial to successful deployment and increasing biometric adoption. The best modality used as unimod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	34,890
2309.05405	Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT	Abdominal organ and tumour segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manual assessment is inherently subjective with considerable inter- and intra-expert variability. In the paper, we propose a hybrid supervised framework, StM...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	391,072
2008.11230	Flood Extent Mapping based on High Resolution Aerial Imagery and DEM: A Hidden Markov Tree Approach	Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challeng...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	193,209
2011.12363	C-Learning: Horizon-Aware Cumulative Accessibility Estimation	Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	208,130
2408.11531	Just Project! Multi-Channel Despeckling, the Easy Way	Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation. While single-channel despeckling has widely benefited from the application of deep learning techniques, extensions to multi-channel SAR ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	482,332
1806.09082	Measuring News Similarity Across Ten U.S. News Sites	News websites make editorial decisions about what stories to include on their website homepages and what stories to emphasize (e.g., large font size for main story). The emphasized stories on a news website are often highly similar to many other news websites (e.g, a terrorist event story). The selective emphasis of a ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	101,290
2002.09811	Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling	In Constraint Programming, constraints are usually represented as predicates allowing or forbidding combinations of values. However, some algorithms exploit a finer representation: error functions. Their usage comes with a price though: it makes problem modeling significantly harder. Here, we propose a method to automa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	165,189
2310.02251	Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving	Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving scenarios have largely focused on a pre-defined (closed) set of object categories and driving scenarios, Talk2BEV blends recent advances in g...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	396,769
2206.08023	AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation	Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medica...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	302,978
2411.00128	Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations	Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth muscle activation data, resulting in a scarcity of datasets. In this work,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,442
2211.13000	A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata	Network modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transport, and many other real world complex systems. In addition, cellular automata (CA) are a formalism that has been studied in the last decades as a model for exploring patterns in the dynamic spatio-...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	332,317
2301.08215	Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient	A foundational problem in reinforcement learning and interactive decision making is to understand what modeling assumptions lead to sample-efficient learning guarantees, and what algorithm design principles achieve optimal sample complexity. Recently, Foster et al. (2021) introduced the Decision-Estimation Coefficient ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,140
1010.0558	Analyzing Network Coding Gossip Made Easy	We give a new technique to analyze the stopping time of gossip protocols that are based on random linear network coding (RLNC). Our analysis drastically simplifies, extends and strengthens previous results. We analyze RLNC gossip in a general framework for network and communication models that encompasses and unifies t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	7,771
2302.09880	Towards Unbounded Machine Unlearning	Deep machine unlearning is the problem of `removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including the key tasks of removing biases (RB), resolving confusion (RC) (caused by mislabelled data in trained models), as well as allowing users to e...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	346,607
2012.04623	Study on the Assessment of the Quality of Experience of Streaming Video	Dynamic adaptive streaming over HTTP provides the work of most multimedia services, however, the nature of this technology further complicates the assessment of the QoE (Quality of Experience). In this paper, the influence of various objective factors on the subjective estimation of the QoE of streaming video is studie...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	210,511
2310.16772	AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning	In urban planning, land use readjustment plays a pivotal role in aligning land use configurations with the current demands for sustainable urban development. However, present-day urban planning practices face two main issues. Firstly, land use decisions are predominantly dependent on human experts. Besides, while resid...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	402,867
2409.05311	Fitting Skeletal Models via Graph-based Learning	Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,720
2303.16974	BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification	Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retri...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	355,060
2409.12892	3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt	We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored Levenberg-Marquardt (LM). Existing methods reduce the optimization time by decreasing the number of Gaussians or by improving the implementation of the differentiable rast...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,758
2004.11077	Quantaized Winograd/Toom-Cook Convolution for DNNs: Beyond Canonical Polynomials Base	The problem how to speed up the convolution computations in Deep Neural Networks is widely investigated in recent years. The Winograd convolution algorithm is a common used method that significantly reduces time consumption. However, it suffers from a problem with numerical accuracy particularly for lower precisions. I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	173,808
2009.07669	Universality Laws for High-Dimensional Learning with Random Features	We prove a universality theorem for learning with random features. Our result shows that, in terms of training and generalization errors, a random feature model with a nonlinear activation function is asymptotically equivalent to a surrogate linear Gaussian model with a matching covariance matrix. This settles a so-cal...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	196,012
2306.05978	3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review	Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among whi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,393
2011.05594	WaDeNet: Wavelet Decomposition based CNN for Speech Processing	Existing speech processing systems consist of different modules, individually optimized for a specific task such as acoustic modelling or feature extraction. In addition to not assuring optimality of the system, the disjoint nature of current speech processing systems make them unsuitable for ubiquitous health applicat...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,976
1910.02830	Open Set Medical Diagnosis	Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	148,351
1909.06137	Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix	We propose a scheme for defending against adversarial attacks by suppressing the largest eigenvalue of the Fisher information matrix (FIM). Our starting point is one explanation on the rationale of adversarial examples. Based on the idea of the difference between a benign sample and its adversarial example is measured ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	145,299
2209.03016	Text Growing on Leaf	Irregular-shaped texts bring challenges to Scene Text Detection (STD). Although existing contour point sequence-based approaches achieve comparable performances, they fail to cover some highly curved ribbon-like text lines. It leads to limited text fitting ability and STD technique application. Considering the above pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,376
2006.05097	GAP++: Learning to generate target-conditioned adversarial examples	Adversarial examples are perturbed inputs which can cause a serious threat for machine learning models. Finding these perturbations is such a hard task that we can only use the iterative methods to traverse. For computational efficiency, recent works use adversarial generative networks to model the distribution of both...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	180,940
2310.01614	Distributed Multi-agent Interaction Generation with Imagined Potential Games	Interactive behavior modeling of multiple agents is an essential challenge in simulation, especially in scenarios when agents need to avoid collisions and cooperate at the same time. Humans can interact with others without explicit communication and navigate in scenarios when cooperation is required. In this work, we a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	396,490
2407.12707	TTSDS -- Text-to-Speech Distribution Score	Many recently published Text-to-Speech (TTS) systems produce audio close to real speech. However, TTS evaluation needs to be revisited to make sense of the results obtained with the new architectures, approaches and datasets. We propose evaluating the quality of synthetic speech as a combination of multiple factors suc...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	474,040
1908.02714	Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images	Relighting of human images has various applications in image synthesis. For relighting, we must infer albedo, shape, and illumination from a human portrait. Previous techniques rely on human faces for this inference, based on spherical harmonics (SH) lighting. However, because they often ignore light occlusion, inferre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	141,072
2203.08775	Practical Conditional Neural Processes Via Tractable Dependent Predictions	Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data. CNPs scale to large datasets and train with ease. Due to these features, CNPs appear well-suited ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,907
2108.03001	Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain	One of the key challenges in Neural Architecture Search (NAS) is to efficiently rank the performances of architectures. The mainstream assessment of performance rankers uses ranking correlations (e.g., Kendall's tau), which pay equal attention to the whole space. However, the optimization goal of NAS is identifying top...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	249,524
math/0009018	Critical Behavior in Lossy Source Coding	The following critical phenomenon was recently discovered. When a memoryless source is compressed using a variable-length fixed-distortion code, the fastest convergence rate of the (pointwise) compression ratio to the optimal $R(D)$ bits/symbol is either $O(\sqrt{n})$ or $O(\log n)$. We show it is always $O(\sqrt{n})$,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,598
2308.15704	Towards a Rigorous Analysis of Mutual Information in Contrastive Learning	Contrastive learning has emerged as a cornerstone in recent achievements of unsupervised representation learning. Its primary paradigm involves an instance discrimination task with a mutual information loss. The loss is known as InfoNCE and it has yielded vital insights into contrastive learning through the lens of mut...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	388,760
1906.08501	A Segmentation-Oriented Inter-Class Transfer Method: Application to Retinal Vessel Segmentation	Retinal vessel segmentation, as a principal nonintrusive diagnose method for ophthalmology diseases or diabetics, suffers from data scarcity due to requiring pixel-wise labels. In this paper, we proposed a convenient patch-based two-stage transfer method. First, based on the information bottleneck theory, we insert one...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,886
2502.08730	New Bounds for Sparse Variational Gaussian Processes	Sparse variational Gaussian processes (GPs) construct tractable posterior approximations to GP models. At the core of these methods is the assumption that the true posterior distribution over training function values ${\bf f}$ and inducing variables ${\bf u}$ is approximated by a variational distribution that incorpora...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,140
1901.06849	Achieving Vanishing Rate Loss in Decentralized Network MIMO	In this paper, we analyze a Network MIMO channel with 2 Transmitters (TXs) jointly serving 2 users, where each TX has a different multi-user Channel State Information (CSI), potentially with a different accuracy. Recently it was shown the surprising result that this decentralized setting can attain the same Degrees-of-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,104
2008.02672	MFNets: Data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources	We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic graph, and is trained via gradient-based minimization of a nonlinear least squares objective. While th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,680
2004.11199	Combining hard and soft decoders for hypergraph product codes	Hypergraph product codes are a class of constant-rate quantum low-density parity-check (LDPC) codes equipped with a linear-time decoder called small-set-flip (SSF). This decoder displays sub-optimal performance in practice and requires very large error correcting codes to be effective. In this work, we present new hybr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	173,846
1808.06048	Distractor-aware Siamese Networks for Visual Object Tracking	Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,461
2310.09277	A Hybrid Approach for Depression Classification: Random Forest-ANN Ensemble on Motor Activity Signals	Regarding the rising number of people suffering from mental health illnesses in today's society, the importance of mental health cannot be overstated. Wearable sensors, which are increasingly widely available, provide a potential way to track and comprehend mental health issues. These gadgets not only monitor everyday ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,719
0803.0053	Mobile Agents for Content-Based WWW Distributed Image Retrieval	At present, the de-facto standard for providing contents in the Internet is the World Wide Web. A technology, which is now emerging on the Web, is Content-Based Image Retrieval (CBIR). CBIR applies methods and algorithms from computer science to analyse and index images based on their visual content. Mobile agents push...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	1,378
cs/0109084	The Internet and Community Networks: Case Studies of Five U.S. Cities	This paper looks at five U.S. cities (Austin, Cleveland, Nashville, Portland, and Washington, DC) and explores strategies being employed by community activists and local governments to create and sustain community networking projects. In some cities, community networking initiatives are relatively mature, while in othe...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	537,426
1405.2906	Load Frequency Control For Distributed Grid Power System Single Area & Multi-area System	This project presents decentralized control scheme for Load-Frequency Control in power System. In this era renewable energy is most promising solution to man's ever increasing energy needs. But the power production by these resources cannot be controlled unlike in thermal plants. A number of optimal control techniques ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	33,033
2010.08055	Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset	Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 {\deg} video analysis. However, the lack of 360 {\deg} datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360{\deg} Kine...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,041
2408.10691	Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches	Since the invention of GPT2--1.5B in 2019, large language models (LLMs) have transitioned from specialized models to versatile foundation models. The LLMs exhibit impressive zero-shot ability, however, require fine-tuning on local datasets and significant resources for deployment. Traditional fine-tuning techniques wit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	481,981
1712.06461	Meeting Energy-Efficient and QoS Requirements of 5G Using D2D Communications	Device-to-device (D2D) communication is a promising technology for the future wireless systems. It allows direct communication between devices, which provides improvements in terms of delay, throughput and energy consumption. Therefore, it can contribute to achieving the ambitious requirements of future 5G wireless sys...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	86,891
2006.02163	Cross-model Back-translated Distillation for Unsupervised Machine Translation	Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently. Crucially, iterative back-translation and denoising auto-encoding for language modeling provide data diversity to train th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	179,971
2408.03079	Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion	Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT's recent success, fine-tuning small models remains the best approach for the ECE task. However, existing fine-tuning based ECE methods cannot address all three key challenges in ECE simultaneously: 1) Complex Causality Ex...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	478,885
2302.03825	Decentralized Riemannian Algorithm for Nonconvex Minimax Problems	The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and deep neural networks with orthogonal weights (Stiefel manifold). Although many optimization algorithms for minimax problems have been develope...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	344,485
1302.6781	A Bayesian Method Reexamined	This paper examines the "K2" network scoring metric of Cooper and Herskovits. It shows counterintuitive results from applying this metric to simple networks. One family of noninformative priors is suggested for assigning equal scores to equivalent networks.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,413
2407.13483	SCAPE: A Simple and Strong Category-Agnostic Pose Estimator	Category-Agnostic Pose Estimation (CAPE) aims to localize keypoints on an object of any category given few exemplars in an in-context manner. Prior arts involve sophisticated designs, e.g., sundry modules for similarity calculation and a two-stage framework, or takes in extra heatmap generation and supervision. We noti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,392
2103.05903	FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing	The development of aerial autonomy has enabled aerial robots to fly agilely in complex environments. However, dodging fast-moving objects in flight remains a challenge, limiting the further application of unmanned aerial vehicles (UAVs). The bottleneck of solving this problem is the accurate perception of rapid dynamic...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	224,121
1805.03797	WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval	With the rise in mobile and voice search, answer passage retrieval acts as a critical component of an effective information retrieval system for open domain question answering. Currently, there are no comparable collections that address non-factoid question answering within larger documents while simultaneously providi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	97,119
1608.06048	Survey of resampling techniques for improving classification performance in unbalanced datasets	A number of classification problems need to deal with data imbalance between classes. Often it is desired to have a high recall on the minority class while maintaining a high precision on the majority class. In this paper, we review a number of resampling techniques proposed in literature to handle unbalanced datasets ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	60,064
2502.09649	Imit Diff: Semantics Guided Diffusion Transformer with Dual Resolution Fusion for Imitation Learning	Visuomotor imitation learning enables embodied agents to effectively acquire manipulation skills from video demonstrations and robot proprioception. However, as scene complexity and visual distractions increase, existing methods that perform well in simple scenes tend to degrade in performance. To address this challeng...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	533,535
2007.01217	Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors	Automated surface segmentation is important and challenging in many medical image analysis applications. Recent deep learning based methods have been developed for various object segmentation tasks. Most of them are a classification based approach, e.g. U-net, which predicts the probability of being target object or ba...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	185,361
2406.05270	fastMRI Breast: A publicly available radial k-space dataset of breast dynamic contrast-enhanced MRI	This data curation work introduces the first large-scale dataset of radial k-space and DICOM data for breast DCE-MRI acquired in diagnostic breast MRI exams. Our dataset includes case-level labels indicating patient age, menopause status, lesion status (negative, benign, and malignant), and lesion type for each case. T...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	462,053
1710.11370	Capacity-Achieving PIR Schemes with Optimal Sub-Packetization	Suppose a database containing $M$ records is replicated across $N$ servers, and a user wants to privately retrieve one record by accessing the servers such that identity of the retrieved record is secret against any up to $T$ servers. A scheme designed for this purpose is called a private information retrieval (PIR) sc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,583
2006.03434	Artificial Intelligence-based Clinical Decision Support for COVID-19 -- Where Art Thou?	The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. While artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, we ide...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	180,312
2309.04564	When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale	Large volumes of text data have contributed significantly to the development of large language models (LLMs) in recent years. This data is typically acquired by scraping the internet, leading to pretraining datasets comprised of noisy web text. To date, efforts to prune these datasets down to a higher quality subset ha...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	390,767
1810.10484	Design of Software Rejuvenation for CPS Security Using Invariant Sets	Software rejuvenation has been proposed as a strategy to protect cyber-physical systems (CSPs) against unanticipated and undetectable cyber attacks. The basic idea is to refresh the system periodically with a secure and trusted copy of the online software so as to eliminate all effects of malicious modifications to the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	111,302
1905.04166	An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs	Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful ex...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	130,388
1708.06022	Learning to Paraphrase for Question Answering	Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,258
2401.15463	DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure	This paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analysis on dataframes, emphasizing safe and non-revealing data handling. Our method, which solely relies on dataframe column names, not only ens...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	424,454
2005.13529	Angle-Dependent Phase Shifter Model for Reconfigurable Intelligent Surfaces: Does the Angle-Reciprocity Hold?	The existing phase shifter models adopted for reconfigurable intelligent surfaces (RISs) have ignored the electromagnetic (EM) waves propagation behavior, thus cannot reveal practical effects of RIS on wireless communication systems. Based on the equivalent circuit, this paper introduces an angle-dependent phase shifte...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,037
2009.08947	Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting	Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describe games or players. Collaborative filtering has been found to be the most accurate predictor of past...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	196,399
2409.13475	PLOT: Text-based Person Search with Part Slot Attention for Corresponding Part Discovery	Text-based person search, employing free-form text queries to identify individuals within a vast image collection, presents a unique challenge in aligning visual and textual representations, particularly at the human part level. Existing methods often struggle with part feature extraction and alignment due to the lack ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,010
2110.06751	Improving the sample-efficiency of neural architecture search with reinforcement learning	Designing complex architectures has been an essential cogwheel in the revolution deep learning has brought about in the past decade. When solving difficult problems in a datadriven manner, a well-tried approach is to take an architecture discovered by renowned deep learning scientists as a basis (e.g. Inception) and tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,731
2305.06055	A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems	Prediction-based decision-making systems are becoming increasingly prevalent in various domains. Previous studies have demonstrated that such systems are vulnerable to runaway feedback loops, e.g., when police are repeatedly sent back to the same neighborhoods regardless of the actual rate of criminal activity, which e...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	363,387
0912.4546	Enhanced Feedback Iterative Decoding of Sparse Quantum Codes	Decoding sparse quantum codes can be accomplished by syndrome-based decoding using a belief propagation (BP) algorithm.We significantly improve this decoding scheme by developing a new feedback adjustment strategy for the standard BP algorithm. In our feedback procedure, we exploit much of the information from stabiliz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,202
2201.01494	Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones	We aim to detect and identify multiple objects using multiple cameras and computer vision for disaster response drones. The major challenges are taming detection errors, resolving ID switching and fragmentation, adapting to multi-scale features and multiple views with global camera motion. Two simple approaches are pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	274,272
2109.08852	Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation	Domain generalizable model is attracting increasing attention in medical image analysis since data is commonly acquired from different institutes with various imaging protocols and scanners. To tackle this challenging domain generalization problem, we propose a Domain Composition and Attention-based network (DCA-Net) t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	256,043
2010.04713	PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline	The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting tumor progression and its treatment response. The value of the Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC), known as the most common cancer in women worldw...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	199,841
1511.01432	Semi-supervised Sequence Learning	We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input seque...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	48,500
2204.04308	Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics	This paper focuses on robotic reinforcement learning with sparse rewards for natural language goal representations. An open problem is the sample-inefficiency that stems from the compositionality of natural language, and from the grounding of language in sensory data and actions. We address these issues with three cont...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	290,604
2211.01202	Human-in-the-Loop Mixup	Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data is proliferating and powering many advances in machine learning; yet, it is not always clear whether synthetic labels are perceptually aligne...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	328,141
2409.06808	Equilibria and Their Stability Do Not Depend on the Control Barrier Function in Safe Optimization-Based Control	Control barrier functions (CBFs) play a critical role in the design of safe optimization-based controllers for control-affine systems. Given a CBF associated with a desired ``safe'' set, the typical approach consists in embedding CBF-based constraints into the optimization problem defining the control law to enforce fo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	487,279
2107.02237	Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination	A recurring theme in statistical learning, online learning, and beyond is that faster convergence rates are possible for problems with low noise, often quantified by the performance of the best hypothesis; such results are known as first-order or small-loss guarantees. While first-order guarantees are relatively well u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,734
2110.03567	GeSERA: General-domain Summary Evaluation by Relevance Analysis	We present GeSERA, an open-source improved version of SERA for evaluating automatic extractive and abstractive summaries from the general domain. SERA is based on a search engine that compares candidate and reference summaries (called queries) against an information retrieval document base (called index). SERA was orig...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	259,546

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